Sentiment Analysis and Text Mining

Sentiment analysis over Twitter offer organizations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. Sentiment analysis overview The emergence of social media has given web users a venue for expressing and sharing their thoughts and opinions on all kinds of topics and events. Twitter, with nearly 600 million users1 and over 250 million messages per day, has quickly become a gold mine for organizations to monitor their reputation and brands by extracting and analyzing the sentiment of the Tweets posted by the public about them, their markets, and competitors. Sentiment analysis has been first introduced by Liu, B. It is also known as opinion mining and subjectivity analysis is the process to determine the attitude or polarity of opinions or reviews written by humans to rate products or services. Sentiment analysis can be applied on any textual form of opinions such as blogs, reviews and Microblogs. Microblogs are those small text messages such as tweets, a short message that cannot exceed 149 characters. These microblogs are easier than other…

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